JV
Jorge Valderrama
2023-05-12
Using R for Data Wrangling, Data Visualization, and Data Analysis
I created this R Markdown document to demonstrate examples of R Code I’ve used for work projects and personal data exploration.
Using R to create animated plots
I wrote the following code to demonstrate differences in COVID-19 hospitalizations among fully vaccinated and unvaccinated adults over time Using data provided by the New York State Department of health (https://coronavirus.health.ny.gov/covid-19-breakthrough-data)
Importing Data
USACOVID <- read.csv ("COVID_Data_R2.csv")Loading Libraries
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(gganimate)Demonstrating Untidy Data
USACOVID## Month Vaccinated.with.updated.booster
## 1 8/1/2022 NA
## 2 8/14/2022 NA
## 3 8/21/2022 NA
## 4 8/28/2022 NA
## 5 9/4/2022 NA
## 6 9/11/2022 NA
## 7 9/18/2022 0.00
## 8 9/25/2022 0.21
## 9 10/2/2022 0.12
## 10 10/9/2022 0.11
## 11 10/16/2022 0.22
## 12 10/23/2022 0.18
## 13 10/30/2022 0.23
## 14 11/6/2022 0.20
## 15 11/13/2022 0.29
## 16 11/20/2022 0.31
## 17 11/27/2022 0.37
## 18 12/4/2022 0.43
## 19 12/11/2022 0.40
## 20 12/18/2022 0.50
## 21 12/25/2022 0.53
## 22 1/1/2023 0.44
## 23 1/8/2023 0.39
## 24 1/15/2023 0.33
## 25 1/22/2023 0.30
## 26 1/29/2023 0.31
## 27 2/5/2023 0.26
## 28 2/12/2023 0.28
## 29 2/19/2023 0.31
## 30 2/26/2023 0.23
## 31 3/5/2023 0.21
## 32 3/12/2023 0.16
## 33 3/19/2023 0.17
## 34 3/26/2023 0.17
## 35 NA
## 36 NA
## 37 NA
## 38 NA
## 39 NA
## 40 NA
## 41 NA
## 42 NA
## Vaccinated.without.updated.booster Unvaccinated
## 1 0.67 3.69
## 2 0.60 3.35
## 3 0.59 3.06
## 4 0.61 3.03
## 5 0.55 2.79
## 6 0.51 2.47
## 7 0.46 2.40
## 8 0.44 2.51
## 9 0.48 2.41
## 10 0.49 2.48
## 11 0.56 2.46
## 12 0.59 2.85
## 13 0.60 2.85
## 14 0.62 2.71
## 15 0.63 2.86
## 16 0.73 3.24
## 17 0.88 4.08
## 18 0.90 4.01
## 19 0.86 3.83
## 20 0.93 4.03
## 21 1.01 4.38
## 22 0.87 3.84
## 23 0.67 3.29
## 24 0.55 2.32
## 25 0.50 2.02
## 26 0.41 2.10
## 27 0.42 1.67
## 28 0.39 1.38
## 29 0.32 1.60
## 30 0.30 1.26
## 31 0.23 1.33
## 32 0.21 0.95
## 33 0.16 0.84
## 34 0.14 0.61
## 35 NA NA
## 36 NA NA
## 37 NA NA
## 38 NA NA
## 39 NA NA
## 40 NA NA
## 41 NA NA
## 42 NA NACreating a new tidy data set
tidyUSACOVID <-USACOVID %>%
pivot_longer(cols = -Month)Demonstrating new tidy data set
tidyUSACOVID## # A tibble: 126 x 3
## Month name value
## <chr> <chr> <dbl>
## 1 8/1/2022 Vaccinated.with.updated.booster NA
## 2 8/1/2022 Vaccinated.without.updated.booster 0.67
## 3 8/1/2022 Unvaccinated 3.69
## 4 8/14/2022 Vaccinated.with.updated.booster NA
## 5 8/14/2022 Vaccinated.without.updated.booster 0.6
## 6 8/14/2022 Unvaccinated 3.35
## 7 8/21/2022 Vaccinated.with.updated.booster NA
## 8 8/21/2022 Vaccinated.without.updated.booster 0.59
## 9 8/21/2022 Unvaccinated 3.06
## 10 8/28/2022 Vaccinated.with.updated.booster NA
## # ... with 116 more rowsLoading lubridate package to change the “Month” column from character type variable to a date type variable
library(lubridate)Changing variable type for “Month” variable
tidyUSACOVID$Month <- mdy(tidyUSACOVID$Month)Demonstrating change in variable type
tidyUSACOVID## # A tibble: 126 x 3
## Month name value
## <date> <chr> <dbl>
## 1 2022-08-01 Vaccinated.with.updated.booster NA
## 2 2022-08-01 Vaccinated.without.updated.booster 0.67
## 3 2022-08-01 Unvaccinated 3.69
## 4 2022-08-14 Vaccinated.with.updated.booster NA
## 5 2022-08-14 Vaccinated.without.updated.booster 0.6
## 6 2022-08-14 Unvaccinated 3.35
## 7 2022-08-21 Vaccinated.with.updated.booster NA
## 8 2022-08-21 Vaccinated.without.updated.booster 0.59
## 9 2022-08-21 Unvaccinated 3.06
## 10 2022-08-28 Vaccinated.with.updated.booster NA
## # ... with 116 more rowsCreating a line graph to demonstrate differences in COVID-19 death rates among fully vaccinated and unvaccinated individuals over time.
ggplot(data=tidyUSACOVID, aes(x=Month, y=value, group=name, color=name))+
geom_line(size = 1.25)+
ggtitle("Rates of COVID-19 Deaths by Vaccination Status in Ages 18 and Older")+
theme_clean()+
theme(axis.text.x=element_text(angle=60, hjust=1, face = "bold"))+
theme(axis.title.y=element_text(size=15,face="bold"))+
theme(plot.title = element_text(hjust = 0.5, lineheight = 0.9))+
theme(plot.title = element_text(face = "bold", size = 15))+
theme(axis.title.x = element_blank())+
theme(legend.title = element_blank())+
theme(legend.text = element_text(face = "bold", size = 10))+
theme(legend.position = "bottom")+
scale_y_continuous(limits=c(0,5))+
labs(y = "Deaths per 100,000 population", subtitle = "August 1, 2022 - March 26, 2023 (23 U.S. Jurisdictions)")+
theme(plot.subtitle = element_text(hjust=0.5))+
scale_color_hue(labels=c('Unvaccinated', 'Vaccinated with updated booster', 'Vaccinated without updated booster'))+
scale_x_date(date_breaks= "1 month", date_labels = "%b %Y", limit=as.Date(c('2022-08-01', '2023-03-26')), expand=c(0,0))
Creating an animated plot
tidyUSACOVID <- na.omit(tidyUSACOVID)
ggplot(data=tidyUSACOVID, aes(x=Month, y=value, group=name, color=name))+
geom_line(size = 1.25)+
ggtitle("Rates of COVID-19 Deaths by Vaccination Status in Ages 18 and Older")+
theme_clean()+
theme(axis.text.x=element_text(angle=60, hjust=1, face = "bold"))+
theme(axis.title.y=element_text(size=15,face="bold"))+
theme(plot.title = element_text(hjust = 0.5, lineheight = 0.9))+
theme(plot.title = element_text(face = "bold", size = 15))+
theme(axis.title.x = element_blank())+
theme(legend.title = element_blank())+
theme(legend.text = element_text(face = "bold", size = 10))+
theme(legend.position = "bottom")+
scale_y_continuous(limits=c(0,5))+
labs(y = "Deaths per 100,000 population", subtitle = "August 1, 2022 - March 26, 2023 (23 U.S. Jurisdictions)")+
theme(plot.subtitle = element_text(hjust=0.5))+
scale_color_hue(labels=c('Unvaccinated', 'Vaccinated with updated booster', 'Vaccinated without updated booster'))+
scale_x_date(date_breaks= "1 month", date_labels = "%b %Y", limit=as.Date(c('2022-08-01', '2023-03-26')), expand=c(0,0))+
transition_reveal(Month)
USACOVIDCASES <- read.csv ("COVID_Data_R3.csv")tidyUSACOVIDCASES <-USACOVIDCASES %>%
pivot_longer(cols = -Month)tidyUSACOVIDCASES## # A tibble: 126 x 3
## Month name value
## <chr> <chr> <dbl>
## 1 8/1/2022 Vaccinated.with.updated.booster NA
## 2 8/1/2022 Vaccinated.without.updated.booster 160.
## 3 8/1/2022 Unvaccinated 468.
## 4 8/14/2022 Vaccinated.with.updated.booster NA
## 5 8/14/2022 Vaccinated.without.updated.booster 144.
## 6 8/14/2022 Unvaccinated 425.
## 7 8/21/2022 Vaccinated.with.updated.booster NA
## 8 8/21/2022 Vaccinated.without.updated.booster 139.
## 9 8/21/2022 Unvaccinated 401.
## 10 8/28/2022 Vaccinated.with.updated.booster NA
## # ... with 116 more rowstidyUSACOVIDCASES$Month <- mdy(tidyUSACOVIDCASES$Month)tidyUSACOVIDCASES## # A tibble: 126 x 3
## Month name value
## <date> <chr> <dbl>
## 1 2022-08-01 Vaccinated.with.updated.booster NA
## 2 2022-08-01 Vaccinated.without.updated.booster 160.
## 3 2022-08-01 Unvaccinated 468.
## 4 2022-08-14 Vaccinated.with.updated.booster NA
## 5 2022-08-14 Vaccinated.without.updated.booster 144.
## 6 2022-08-14 Unvaccinated 425.
## 7 2022-08-21 Vaccinated.with.updated.booster NA
## 8 2022-08-21 Vaccinated.without.updated.booster 139.
## 9 2022-08-21 Unvaccinated 401.
## 10 2022-08-28 Vaccinated.with.updated.booster NA
## # ... with 116 more rowsggplot(data=tidyUSACOVIDCASES, aes(x=Month, y=value, group=name, color=name))+
geom_line(size = 1.25)+
ggtitle("Rates of COVID-19 Cases by Vaccination Status in Ages 18 and Older")+
theme_clean()+
theme(axis.text.x=element_text(angle=60, hjust=1, face = "bold"))+
theme(axis.title.y=element_text(size=15,face="bold"))+
theme(plot.title = element_text(hjust = 0.5, lineheight = 0.9))+
theme(plot.title = element_text(face = "bold", size = 15))+
theme(axis.title.x = element_blank())+
theme(legend.title = element_blank())+
theme(legend.text = element_text(face = "bold", size = 10))+
theme(legend.position = "bottom")+
scale_y_continuous(limits=c(0,600))+
labs(y = "Cases per 100,000 population", subtitle = "August 1, 2022 - April 16, 2023 (24 U.S. Jurisdictions)")+
theme(plot.subtitle = element_text(hjust=0.5))+
scale_color_hue(labels=c('Unvaccinated', 'Vaccinated with updated booster', 'Vaccinated without updated booster'))+
scale_x_date(date_breaks= "1 month", date_labels = "%b %Y", limit=as.Date(c('2022-08-01', '2023-04-16')), expand=c(0,0))
```